• Journal of Korean Society of Transportation
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• v.10 no.2
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• pp.25-42
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• 1992

The purpose of this paper is to construct a model to compute a progression adjustment factor on a signalized network. In a way to construct the model, a simulation method is introduced and the TRAF-NETSIM is used as a tool of simulation. The structure of the network chooses an urban arterial network so as to measure the effect of progression and compute average stopped delay on each link. A regression model is constructed by using the results of the simulation. The stepwise variable selection in the regression model in used. The findings of this paper are as follows: i)The secondary queue and platoon ratio are sensitive to the values of the progression adjustment factor ii) The continuous model can practically reflect on various situations in the real world. The platoon adjustment factor can be computed by this model and the data required for this model can be easily obtained in the field.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.125-132
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• 2015

It is current situation that a number of issues are being raised how the weight is calculated from customer satisfaction survey. This study investigated how the weight of satisfaction for each quantile is different by comparing ordinary least square regression model to quantile regression model and carried out bootstrap verification to find the influence difference of regression coefficient for each quantile. As the analysis result of using R(Quantreg package) that is open software, it appeared that there was the influence size of satisfaction factor along study result and quantile and there was the significant difference statistically regarding regression coefficient for each quantile. So, to use quantile regression model that offers the influence of satisfaction factor for each customer group along satisfaction level would contribute to plan the quantitative convergence policy for customer satisfaction.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.689-694
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• 2004

We investigate the test for model adequacy in nonlinear regression. We can expect the usual likelihood ratio statistic to be unaffected by any parametric- effect curvature; only the effect of intrinsic curvature needs to be considered. Multiplicative correction factor is derived for the limiting distribution of test statistic, which is a function of the intrinsic curvature arrays.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.387-392
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• 1999

In this paper, we attempted to eatablish questionnaire items for evaluation of residential satisfaction level by factor analysis, and the model was developed as a function of primary component of questionnaire items. For development of residential satisfaction model, items are selected by factor analysis adn regression coefficient is estimated by the multiple linear regression analysis.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.719-729
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• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.52-61
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• 2021

This study using experimental design and linear regression technique was implemented in order to predict the pitting potential of stainless steel in marine environments, with the target materials being AL-6XN and STS 316L. The various variables (inputs) which affect stainless steel's pitting potential included the pitting resistance equivalent number (PRNE), temperature, pH, Cl^- concentration, sulfate levels, and nitrate levels. Among them, significant factors affecting pitting potential were chosen through an experimental design method (screening design, full factor design, analysis of variance). The potentiodynamic polarization test was performed based on the experimental design, including significant factor levels. From these testing methods, a total 32 polarization curves were obtained, which were used as training data for the linear regression model. As a result of the model's validation, it showed an acceptable prediction performance, which was statistically significant within the 95% confidence level. The linear regression model based on the full factorial design and ANOVA also showed a high confidence level in the prediction of pitting potential. This study confirmed the possibility to predict the pitting potential of stainless steel according to various variables used with experimental linear regression design.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.169-189
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• 2004

Under the assumption of default priors, such as noninformative priors, Bayesian model determination and parameter estimation of regression models with stationary and invertible ARMA errors are developed under exact full likelihoods. The default Bayes factors, the fractional Bayes factor (FBF) of O'Hagan (1995) and the arithmetic intrinsic Bayes factors (AIBF) of Berger and Pericchi (1996a), are used as tools for the selection of the Bayesian model. Bayesian estimates are obtained by running the Metropolis-Hastings subchain in the Gibbs sampler. Finally, the results of numerical studies, designed to check the performance of the theoretical results discussed here, are presented.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.760-768
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• 2017

The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.189-199
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• 2020

This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.